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Review Matrix Metalloproteases in Pancreatic Ductal Adenocarcinoma: Key Drivers of Disease Progression?

Etienne J. Slapak 1,2,3, JanWillem Duitman 1,2, Cansu Tekin 1,2,3 , Maarten F. Bijlsma 2,3 and C. Arnold Spek 1,2,*

1 Center of Experimental and Molecular Medicine, University of Amsterdam, Amsterdam UMC, 1105 AZ Amsterdam, The Netherlands; [email protected] (E.J.S.); [email protected] (J.D.); [email protected] (C.T.) 2 Laboratory for Experimental Oncology and Radiobiology, Cancer Center Amsterdam, University of Amsterdam, Amsterdam UMC, 1105 AZ Amsterdam, The Netherlands; [email protected] 3 Oncode Institute, 1105 AZ Amsterdam, The Netherlands * Correspondence: [email protected]

 Received: 26 March 2020; Accepted: 15 April 2020; Published: 18 April 2020 

Abstract: Pancreatic cancer is a dismal disorder that is histologically characterized by a dense fibrotic stroma around the tumor cells. As the comprises the bulk of the stroma, matrix degrading may play an important role in pancreatic cancer. It has been suggested that matrix metalloproteases are key drivers of both tumor growth and during pancreatic cancer progression. Based upon this notion, changes in matrix metalloprotease expression levels are often considered surrogate markers for pancreatic cancer progression and/or treatment response. Indeed, reduced matrix metalloprotease levels upon treatment (either pharmacological or due to genetic ablation) are considered as proof of the anti-tumorigenic potential of the mediator under study. In the current review, we aim to establish whether matrix metalloproteases indeed drive pancreatic cancer progression and whether decreased matrix metalloprotease levels in experimental settings are therefore indicative of treatment response. After a systematic review of the studies focusing on matrix metalloproteases in pancreatic cancer, we conclude that the available literature is not as convincing as expected and that, although individual matrix metalloproteases may contribute to pancreatic cancer growth and metastasis, this does not support the generalized notion that matrix metalloproteases drive pancreatic ductal adenocarcinoma progression.

Keywords: MMP; MMP2; MMP9; MMP7; MMP14; matrix metalloproteases; PDAC; pancreatic cancer

1. Introduction Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with the worst survival outcome of any cancer [1]. Its incidence, which is around 10 per 100,000 individuals, is rising in developed countries [2,3], with 458 thousand new cases and 432 thousand deaths in 2018 worldwide [4]. The 5-year survival rate is around 9%, and the 10-year mortality is approaching 99% [5]. Progress towards improving survival has been slow, and current treatment options are inadequate. The only significant progress that has been made is in the form of lower mortality rates for patients eligible for resections, and a slight prolongation and improved quality of life in patients with inoperable disease with the use of chemotherapeutic agents. Single-agent gemcitabine treatment has been the standard of care for inoperable PDAC for many years, although the observed benefits are small in daily practice [6–9] and seem restricted to patients with a good performance status [10]. More recently, nanoparticle albumin-bound paclitaxel was shown to exert superior antitumor activity compared to gemcitabine monotherapy, thereby establishing nab-paclitaxel and gemcitabine combination therapy

Biology 2020, 9, 80; doi:10.3390/biology9040080 www.mdpi.com/journal/biology Biology 2020, 9, 80 2 of 21 as first-line chemotherapy regimens in PDAC [11]. In patients with a good performance status, combination therapy with folinic acid, fluorouracil, irinotecan and oxaliplatin (FOLFIRINOX) is superior over other treatments [12] and FOLFIRINOX is consequently emerging as the new standard of care for relatively fit patients [13]. Importantly however, even in the specific group of patients eligible for FOLFIRINOX treatment, the survival benefit is limited [14].

1.1. Tumor Microenvironment of PDAC PDAC is characterized by a strong desmoplastic reaction, which results in an archetypal tumor microenvironment, consisting of a dense stroma surrounding the tumor cells [15,16]. The stroma forms the bulk of the tumor, taking up to 90% of the total tumor mass and consists of many cellular and acellular components like (myo)fibroblasts, macrophages, blood vessels and extracellular matrix components such as, among others, I, collagen IV, and fibronectin. In the stroma, the extracellular matrix has traditionally been considered to be a stable structure that mainly plays a supportive role in maintaining tissue morphology. Nowadays, however, it is evident that the extracellular matrix forms a dynamic and versatile milieu that affects the fundamental processes of the surrounding cells [17,18]. Accordingly, the loss of extracellular matrix homeostasis and integrity is considered one of the hallmarks of cancer and typically defines transitional events, resulting in cancer progression and metastasis [19]. Moreover, the loss of extracellular matrix homeostasis due to stromal depletion aggravates pancreatic cancer progression in preclinical animal models [20–22].

1.2. Matrix Metalloproteases in the Tumor Microenvironment The desmoplastic PDAC stroma contains many different proteases that play a key role in the crosstalk between tumor and stromal cells. An intriguing group of proteases in the tumor microenvironment consist of matrix metalloproteases (MMPs), which are primarily known for their ability to degrade extracellular matrix components. Altered expression and/or activity of MMPs in the tumor microenvironment is likely to lead to the loss of homeostasis of the extracellular matrix, thereby driving PDAC progression. Based upon this notion, MMPs are considered important contributors to PDAC progression and experimental PDAC studies frequently use MMPs as surrogate markers for treatment responses. Decreased MMP levels are, nowadays, considered as important signs of the anti-tumorigenic potential of the /compound/miRNA under study. In the current review, we address whether the literature supports the concept that MMPs drive PDAC progression and if decreased MMP levels under experimental settings are indicative of the treatment response. To this end, we performed a systematic review of patient and experimental animal studies, focusing on MMPs in PDAC.

1.3. Overview of Matrix Metalloproteases MMPs are calcium-dependent -containing of the metzincin superfamily. They typically contain an N-terminal propeptide of approximately 80–90 amino acids, with a conserved PRCGXPD motif that is responsible for maintaining latency via the binding of the cysteine residue to the zinc atom in the [23]. After the proteolytic removal of the propeptide, the active form of MMP contains a calcium-dependent catalytic domain of around 200 amino acids, which contains a hydrophobic S10-pocket that determines substrate specificity, proceeded by a linker region of variable length, and the C-terminal -like domain, which spans approximately 200 amino acids. The hemopexin-like domain, which is absent in some MMP family members, plays a functional role in substrate binding and/or in interactions with tissue inhibitors of metalloproteases (TIMPs), a family of specific MMP inhibitors [24]. Since the identification of a diffusible collagenolytic factor in living amphibian tissue that is capable of degrading undenatured calf skin collagen [25], a total of 24 MMPs have been identified in humans [26]. According to their substrate specificity, MMPs are classified into subfamilies: (1) , (2) , (3) stromelysins, (4) matrilysins, (5) membrane-type MMPs and Biology 2020, 9, 80 3 of 21

(6) others. Despite the general acceptance of the classification system based on extracellular matrix substrates, MMPs are rather promiscuous in substrate recognition and also proteolytically cleave substrates beyond extracellular matrix .

2. Methods To provide a comprehensive overview of the role of MMPs in PDAC, a systematic PubMed search without restrictions was performed. A combination of the search terms “pancreatic cancer” and every individual MMP (both using the official gene name and the common name; see Supplementary Materials Table S1) was used to retrieve papers published up to 1 March 2020. All papers were independently screened by their title and abstract, followed by full text assessment to include papers that contained MMP expression analysis in PDAC patients and papers that contained animal experiments that targeted (either genetically or pharmacologically) MMPs in pancreatic cancer models. The excluded papers were those that contained in vitro data only, papers that assayed MMP levels in experimental animal models without interventions or genetic modifications, or papers that did not focus on PDAC.

3. Results We retrieved 64 papers focusing on collagenases, 642 papers focusing on gelatinases, 51 papers focusing on stromelysins, 93 papers focusing on matrilysins, 66 papers focusing on transmembrane MMPs and 21 papers focusing on other MMPs (Figure1). After the removal of duplicates, 816 eligible studies were identified and were vigorously screened to obtain those that contained patient data and/or animal experiments in which MMPs were targeted. This resulted in the inclusion of 14 papers focusing on collagenases, 60 on gelatinases, 11 on stromelysins, 21 on matrilysins, 12 on transmembrane-type MMPs and five on the so-called “other” MMPs. As several of the eligible papers contained data on multiple MMPs, the total number of papers including patient/experimental animal data selected for the review was 91. Biology 2020, 9, 80 4 of 21 Biology 2020, 9, x FOR PEER REVIEW 4 of 20

FigureFigure 1. Flowchart 1. Flowchart of paper of paper inclusion. inclusion. Using Using the the search sear criteriach criteria indicated indicated in Supplementaryin Supplemental Materials Table S1, Table we obtained S1, we obtained 814 eligible 814 papers eligible tha paperst we thatscreened we screened for the for the presencepresence of patient and/or and/or matrix metallopr metalloproteaseotease (MMP) (MMP) intervention intervention in in animal animal models. models. After After the the exclusion exclusion of duplicate of duplicate papers, papers, we weended ended up with up with 91 papers 91 papers that that werewere included included in the in the review. review.

Biology 2020, 9, 80 5 of 21

3.1. Collagenases in PDAC Despite the general notion that collagenases (MMP1, MMP8 and MMP13) are key players in cancer biology [27–29], relatively little is known about collagenases in PDAC. Although MMP-1 is consistently shown to be overexpressed in PDAC patients compared to healthy controls [30–36], its effects on cancer progression are inconsistent (Table1). For example, MMP1 overexpression has been reported as being associated with both a poor prognosis [30] and prolonged survival [37], although no correlations with tumor size, differentiation status and lymph node involvement have been observed [30,36,38]. Despite an elegant recent study showing that MMP1-dependent protease activated receptor (PAR)-1 drives PDAC cell migration and perineural invasion [33], the important role of MMP1 in PDAC is not supported by the experimental data. Besides MMP1 overexpression, MMP8 [36,39] and MMP13 [34,40] are also overexpressed in PDAC patients compared to healthy controls. The relevance of increased MMP expression is not well documented and only a single study showed that MMP-13 expression is associated with lymph node metastasis and the tumor’s pathological stage [41]. Interestingly however, MMP13 overexpression significantly promoted the invasion of the PDAC cells in vitro, whereas MMP13 inhibition blocked leptin-mediated PDAC cell invasion [41], while CD40 agonist-dependent resolution of fibrosis and enhanced chemotherapy efficacy were diminished by MMP13 inhibition [42].

3.2. Gelatinases in PDAC The most studied MMPs in PDAC are, without a doubt, the gelatinases (MMP2 and MMP9; see Figure1). The vast majority of studies show that both MMP2 [ 34,35,43–62] and MMP9 [34,36,39,48,49,53,54,59,62–67] are upregulated in PDAC patients (Table1), while a minority of studies fail to show a difference in expression between PDAC and the controls [36,38,52,60,61,68–71]. The potential clinical relevance is less pronounced, as just half of the studies reported associations between increased MMP2 or MMP9 levels with clinical characteristics such as survival, metastasis or tumor stage [43,46–48,50,51,53,56–58,61,63,65,67,68,72,73], whereas in the other half of the studies no such correlations were observed (Table2). Despite the rather diverse observations in patients, initial preclinical experimental animal experiments showed promising results (Table3). Batimastat treatment of mice harboring orthotopic pancreatic cancers reduced cancer growth, metastasis and death compared to control-treated mice, while also potentiating gemcitabine sensitivity [74–77]. Batimastat was also shown to reduce metastasis and death when PDAC cells were directly injected into the spleen of recipient mice, in order to mimic liver metastasis in PDAC [78]. Although batimastat is not specific to MMP2 and MMP9 and also inhibits MMP1, MMP3, MMP7, MMP8 and several ADAM family members, based on the gelatin zymography of tumor samples before and after treatment, it was hypothesized that the tumor-inhibiting effect of batimastat was dependent on MMP2 and, to a lesser extent, MMP9. The potential importance of MMP2 and MMP9 in PDAC progression is further supported by studies using more specific inhibitors like MMI-166, RO28-2653 and OPB-3206. Indeed, the selective MMP2, MMP9 and MMP14 inhibitor MMI-166 inhibited PDAC growth in both mice and Syrian hamsters [79,80], whereas RO28-2653 and OPB-3206 (both also selective MMP2, MMP9 and MMP14 inhibitors) reduced chemically induced pancreatic carcinogenesis in Syrian hamsters [81,82]. Finally, treatment with the selective MMP2 and MMP9 inhibitor SB-3CT reduced the lung metastasis of subcutaneously implanted PDAC cells [83]. The most conclusive evidence of the role of MMP2 in PDAC progression comes from subcutaneous models, in which the injection of shMMP2-silenced PANC1 cells resulted in smaller tumors compared to the injection of control shRNA transduced cells [84], whereas treatment with MMP2-blocking peptides limited tumor growth and [85]. In a similar way to the inconclusive association studies in patients (see above and Table2), experimental animal experiments specifically targeting MMP9 show inconsistent results (Table3). Orthotopic injections of MMP9-overexpressing Panc02 cells led to bigger tumors than injections of their control counterparts, but the absence/presence of MMP9 did not affect metastasis [86]. Treatment with a MMP9-blocking antibody did not affect the tumor growth of subcutaneously implanted PDAC Biology 2020, 9, 80 6 of 21 cells, but did enhance gemcitabine and nab-paclitaxel sensitivity when PDAC cells were injected into the peritoneal cavity [87]. Doxycycline treatment, suggested to specifically target MMP9, reduced the growth of subcutaneously injected Capan-1 cells [88]. Finally, subcutaneous or orthotopic implantation of PDAC cells in MMP9-deficient mice diminished tumor take, tumor growth, angiogenesis and metastasis [83,89] but tumor progression and metastasis increased in MMP9-deficient mice on the Kras(G12D)/Tp53 background [90].

3.3. Stromelysins in PDAC Clinical studies do not support the general role of stromelysins (MMP3, MMP10 and MMP11) in PDAC (Table1). Although MMP11 is consistently upregulated and associated with clinical characteristics in PDAC patients [35,36,91–93], the data for MMP3 is more controversial. Only half of the studies focusing on MMP3 suggest its expression is increased in PDAC patients compared to control tissue [34,35,94,95], and only a single study suggests that MMP3 is associated with patient survival [95]. Besides clinical studies, preclinical animal models also do not support an important role for stromelysins in PDAC progression. Apart from a study which suggests, but does not prove, that MMP10 drives the invasion and metastasis of PDAC [96], it has only been shown that MMP3 overexpression on the Kras(G12D) background increases neoplastic alterations in pancreatic acinar cells [94]. These premalignant morphological changes were accompanied by the recruitment of infiltrating immune cells and the expression of smooth muscle actin and collagen, indicating that MMP3 is not only a coconspirator of Kras in inducing tumorigenic changes in epithelial cells, but also that it promotes the establishment of a tumorigenic microenvironment. Though it has been suggested that MMP3 may play a role in PDAC initiation, the actual importance of endogenous MMP3 (as opposed to overexpressed MMP3) in PDAC progression and its potential clinical relevance remains elusive.

3.4. Matrilysins in PDAC MMP7 and MMP26 are the only two members of the matrilysin subfamily. A large number of studies have compared MMP7 expression in PDAC patients with pancreatitis patients and/or healthy controls and have consistently shown that MMP7 levels are elevated in PDAC patients (Table1)[ 34–36,54,69,91,97–104]. More importantly, MMP7 levels correlate with metastasis and/or survival in most, but not all, studies. Based upon these reports, it is suggested that MMP7 is an important regulator of tumor formation. In line with this notion, preclinical experimental animal models show that MMP7 expression is intimately linked with acinar-to-ductal metaplasia and that pancreatic duct ligation-dependent acinar cell loss, caspase-3 activation, and subsequent metaplasia is significantly reduced in MMP7-deficient mice (Table3)[ 98]. The effect of MMP7 on acinar-to-ductal metaplasia seems model-specific, however, as MMP7 deficiency did not affect pancreatitis driven-PanIN development in Pfta1-Cre Kras(G12D) mice [105]. In addition to PDAC initiation, MMP7 also seems to drive PDAC progression. Using several genetic Kras-driven PDAC models, it was shown that both tumor size and metastasis were significantly reduced by MMP7 deficiency. The percentage of mice with lymph node metastasis reduced from around 60 in MMP7-proficient mice to 0 in MMP7-deficient mice, whereas the percentage of mice with liver metastasis dropped from 67% to 13% due to MMP7 deficiency [105]. In line with these findings, the metastasis of MMP7-silenced PANC1 cells was largely reduced compared to control PANC1 cells, whereas pharmacological MMP7 inhibition with sulfur-2-(4-chlorine-3-trifluoromethyl phenyl)-sulfonamido-4-phenylbutyric acid (SCTPSPA) also significantly reduced the metastasis of PANC1 cells [101]. MMP26 expression was also induced in PDAC patients compared to the controls and, intriguingly, MMP26 was expressed significantly more often in tumors with lymph node involvement. Although this is suggestive of the general role of matrilysins in PDAC progression, experimental data confirming the pro-tumorigenic role of MMP26 in PDAC is lacking and it remains to be established whether MMP26 is indeed a driver of disease progression or merely acts as a marker of PDAC metastasis [106]. Biology 2020, 9, 80 7 of 21

3.5. Membrane-Type MMPs in PDAC Seven membrane-bound MMPs have been described so far: the transmembrane members MMP14, MMP15, MMP16, MMP23 and MMP24, and the GPI-anchored members MMP17 and MMP25. Of the membrane-bound MMPs, MMP14 seems most relevant in the setting of PDAC (Tables1–3). Indeed, the overexpression of MMP14 in mice expressing an activating Kras(G12D) mutation led to more large, dysplastic mucin-containing papillary lesions compared to the control Kras(G12D) mice (Table3)[ 107]. Using subcutaneous models, MMP14 overexpression in cancer cells seems to reduce the cytotoxic effect of gemcitabine [108], whereas MMP14 inhibition in pancreatic stellate cells limits tumor growth [84]. Moreover, the cancer cell-specific overexpression of membrane-type 1 matrix cytoplasmic tail binding protein-1 (MTCBP-1; MMP14 binding protein inhibiting its activity) restricts metastasis in orthotopic PDAC models, further suggesting that MMP14 may enhance tumor progression [109]. However, clinical data do not support the important role of MMP14 in PDAC progression (Tables1 and2). Although MMP14 may be overexpressed in PDAC [ 44,110], MMP14 does not correlate with clinical characteristics such as tumor differentiation, tumor size, lymph node status, or patient survival [31,37,111].

3.6. Other MMPs in PDAC The so-called other MMPs (i.e., MMP12, MMP19, MMP20, MMP21, MMP27 and MMP28) are not very well characterized in PDAC. Although some members seem to be overexpressed in PDAC [106,111,112] and may be associated with tumor stage and patients survival (Table1)[ 111–113], no preclinical studies have addressed the role of these MMPs in PDAC (Table2). Therefore, their actual importance remains to be established.

3.7. Clinical Trials with MMP Inhibitors in PDAC Only two phase 3 trials focusing on MMP inhibition in PDAC have been published [114,115]. One trial showed that the addition of marimastat (a broad-spectrum MMP inhibitor targeting MMP1, MMP2, MMP7, MMP9 and MMP14) to gemcitabine in a double-blind placebo-controlled, randomized study was well-tolerated but did not show clinical benefits in PDAC patients [114]. The overall response rates (11% and 16% with and without the addition of marimastat, respectively), progression-free survival and time to treatment failure were similar in both treatment arms. Another phase 3 trial showed that BAY 12-9566 (tanomastat; MMP2, MMP3 and MMP9 inhibitor) treatment was also well tolerated by PDAC patients but was inferior to gemcitabine, with median survival times of 3.74 and 6.59 months for the BAY 12-9566 and gemcitabine arm, respectively [115]. Median progression-free survival and quality-of-life analyses also favored gemcitabine, arguing against MMP inhibition in the setting of PDAC. The fact that there are no clinical benefits obtained through MMP inhibition does not imply that MMPs do not contribute to PDAC progression. As elegantly discussed [116,117], the disappointing clinical trial results may be due to several reasons, of which the inclusion of advanced stage disease seems most relevant. Broad spectrum MMP inhibitors may also lack efficacy as they could block the potential tumor inhibitory activities of specific MMPs. As indicated above, MMP9 deficiency on the Kras(G12D) background enhanced tumor progression and invasive growth [90], supporting this notion and providing an alternative explanation for the negative marimastat and BAY 12-9566 results in PDAC patients. Finally, the poor clinical efficacy of MMP inhibitors could also be explained by the overestimation of the role of MMPs in PDAC progression based on preclinical models that do not fully capture the complexity of human disease. Biology 2020, 9, 80 8 of 21

Table 1. MMP expression levels in Pancreatic ductal adenocarcinoma (PDAC) patients and controls. Red indicates increased MMP levels, blue indicates no difference and green indicates decreased MMP levels in PDAC patients.

Member Patient Number Method Difference Reference MMP1 45 PC, 10 CO IHC no difference [36] MMP1 8 PC, 8 CO RNA no difference [59] MMP1 248 PC, 216 CO Serum no difference [69] MMP1 46 PC, 5 CO IHC up vs healthy [30] MMP1 25 PC RNA up vs adjacent CO [31] MMP1 10 PC, 12 CP, 5 CO IHC up vs CO [32] MMP1 45 PC RNA up vs adjacent CO [33] MMP1 30 PC IHC up vs adjacent CO [33] MMP1 104 PC, 62 CO IHC up vs CO [34] MMP1 17 PC, 17 CO RNA up vs CO [35] MMP1 18 PC, 8 CO RNA up vs healthy [36] MMP2 75 PC, 10 CO IHC no difference [36] MMP2 18 PC, 8 CO RNA no difference [36] MMP2 70 PC and 10 CO IHC no difference [38] MMP2 92 PC, 43 CP, 91 CO Serum no difference [68] MMP2 35 PC RNA/IHC no difference [70] MMP2 46 PC, 13 CO Serum no difference [71] MMP2 104 PC, 62 CO IHC up vs CO [34] MMP2 17 PC, 17 CO RNA upvsCO [35] MMP2 122 PC IHC up vs adjacent CO [43] MMP2 18 PC, 9 CP, 9 CO RNA up vs both others [44] MMP2 12 PC, 11 CP, 7 CO pancreatic juice up vs both others [45] MMP2 127 PC IHC up vs CO [46] MMP2 20 PC IHC up vs CO [47] MMP2 32 PC, 31 CP ELISA on tissue up vs CP [48] MMP2 110 PC, 24 BT Plasma up vs BT [49] MMP2 37 PC, 7 CP IHC up vs CP and CO [50] MMP2 45 PC IHC up vs CO [51] MMP2 51 PC, 60 CO Urine up vs CO [52] MMP2 44 PC, 8 CO IHC up vs CO [52] MMP2 30 PC, 17 CO IHC up vs CO [53] MMP2 29 PC IHC up vs adjacent CO [54] MMP2 127 PC, 25 CP, 25 CO Plasma up vs CP and CO [55] MMP2 106 PC RNA/WB up vs adjacent CO [56] MMP2 40 PC, 10 CO IHC up vs CO [57] MMP2 67 PC, 20 CO IHC up vs adjacent CO [58] MMP2 8 PC, 8 CO RNA upvsCO [59] MMP2 10 PC, 3 CO ZG upvsCO [60] MMP2 33 PC, 14 CP, 13 CO ZG/WB upvsCO [61] MMP2 22 PC, 9 CP, 9 CO RNA up vs adjacent CO [62] MMP2 10 PC, 213 CO Serum down vs CO [118] MMP3 45 PC, 10 CO IHC no difference [36] MMP3 18 PC, 8 CO RNA no difference [36] MMP3 8 PC, 8 CO RNA no difference [59] MMP3 104 PC, 62 CO IHC up vs CO [34] MMP3 17 PC, 17 CO RNA up vs CO [35] MMP3 140 PC, 12 CO IHC up vs CO [94] MMP3 140 PC, 12 CO IHC up vs CO [95] MMP7 18 PC, 8 CO RNA no difference [36] MMP7 104 PC, 62 CO IHC up vs CO [34] MMP7 17 PC, 17 CO RNA up vs CO [35] MMP7 45 PC, 10 CO IHC up vs CO [36] MMP7 29 PC IHC up vs adjacent CO [54] MMP7 248 PC, 216 CO Serum up vs CO [68] Biology 2020, 9, 80 9 of 21

Table 1. Cont.

Member Patient Number Method Difference Reference MMP7 44 PC, 17 CP RNA up vs CP [91] MMP7 70 PC RNA up vs adjacent CO [97] MMP7 32 PC, ? CO IHC up vs CO [98] MMP7 47 PC, 10 CO IHC up vs CO [99] MMP7 63 PC, 31 CP Plasma up vs CP [100] MMP7 30 PC RNA up vs adjacent CO [101] MMP7 5 PC, 5 CP, 62 CO IHC up vs CP and CO [102] MMP7 131 PC, 30 CP, 131 CO Plasma up vs CO [103] MMP7 10 PC RNA up vs adjacent CO [104] MMP8 248 PC, 216 CO Serum no difference [69] MMP8 75 PC, 10 CO IHC up vs CO [36] MMP8 91 PC, 41 CP, 30 CO RNA (PBMCs) up vs CO [39] MMP9 18 PC, 8 CO RNA no difference [36] MMP9 70 PC, 10 CO IHC no difference [38] MMP9 51 PC, 60 CO urine no difference [52] MMP9 10 PC, 3 CO ZG no difference [60] MMP9 33 PC, 14 CP, 13 CO ZG/WB no difference [61] MMP9 248 PC, 216 CO Serum no difference [69] MMP9 35 PC RNA/IHC no difference [70] MMP9 104 PC, 62 CO IHC up vs CO [34] MMP9 45 PC, 10 CO IHC up vs CO [36] MMP9 91 PC, 41 CP, 30 CO RNA (PBMCs) up vs CP and CO [39] MMP9 32 PC, 31 CP ELISA on tissue up vs CP [48] MMP9 110 PC, 24 BT Plasma up vs BT [49] MMP9 30 PC, 17 CO IHC up vs CO [53] MMP9 29 PC IHC up vs adjacent CO [54] MMP9 8 PC, 8 CO RNA up vs CO [59] MMP9 22 PC, 9 CP, 9 CO RNA up vs adjacent CO [62] MMP9 36 PC IHC up vs CO [63] MMP9 9 PC, 9 CO MS/MS up vs CO [64] MMP9 78 PC, 45 CP, 70 CO Serum up vs both [65] MMP9 62 PC, 16 CO IHC up vs CO [66] MMP9 103 PC, 6 CO IHC up vs CO [67] MMP10 17 PC, 17 CO RNA no difference [35] MMP11 17 PC, 17 CO RNA up vs CO [35] MMP11 18 PC, 8 CO RNA up vs CO [36] MMP11 75 PC, 10 CO IHC up vs CO [36] MMP11 44 PC, 17 CP RNA up vs CP [91] MMP11 12 PC, 16 CO Blood up vs CO [92] MMP11 21 PC, 9 CO IHC up vs CO [93] MMP12 75 PC, 10 CO IHC no difference [36] MMP12 39 PC, 13 CO RNA/WB/IHC up vs CO [111] MMP13 104 PC, 62 CO IHC up vs CO [34] MMP13 45 PC RNA up vs adjacent CO [40] MMP14 75 PC, 10 CO IHC no difference [36] MMP14 35 PC RNA/IHC no difference [111] MMP14 18 PC, 9 CP, 9 CO RNA up vs both others [44] MMP14 64 PC, 9 CO IHC up vs CO [110] MMP15 18 PC, 9 CP, 9 CO RNA up vs both others [44] MMP15 18 PC, 8 CO RNA reduced vs CO [36] MMP16 18 PC, 9 CP, 9 CO RNA no difference [44] MMP16 12 PC IHC up vs adjacent CO [119] MMP19 102 PC IHC up vs adjacent CO [112] MMP20 102 PC IHC up vs adjacent CO [112] MMP21 25 PC, 18 CO IHC up vs CO [106] MMP26 25 PC, 18 CO IHC up vs CO [106] Pancreatic cancer (PC); pancreatitis (CP); healthy control (CO); benign tumor (BT); immunohistochemistry (IHC); Western blot (WB); zymography (DG). Biology 2020, 9, 80 10 of 21

Table 2. Association between MMP expression and clinical characteristics of PDAC. Red indicates that MMP levels are associated with poor outcome, blue indicates no association and green indicates that MMP levels are associated with improved survival.

Member Patient Number Method Correlation Reference MMP1 45 PC, 10 CO IHC no [36] MMP1 70 PC IHC no [38] MMP1 46 PC, 5 CO IHC OS, LM, Size, Stage [30] MMP1 30 PC IHC PNI [33] MMP1 51 PC IHC/serum OS [37] MMP2 75 PC, 10 CO IHC no [36] MMP2 70 PC, 10 CO IHC no [38] MMP2 51 PC IHC/serum no [37] MMP2 32 PC, 31 CP ELISA on tissue no [48] MMP2 37 PC, 7 CP IHC no [50] MMP2 29 PC IHC no [54] MMP2 127 PC, 25 CP, 25 CO plasma no [55] MMP2 35 PC RNA/IHC no [70] MMP2 32 PC IHC no [120] MMP2 67 PC IHC LM, PNI, OS, DF [121] MMP2 122 PC IHC OS, DF [43] MMP2 127 PC IHC OS, Stage [46] MMP2 20 PC IHC LM [47] MMP2 37 PC, 7 CP IHC LM, DM [50] MMP2 45 PC IHC OS, LM, Stage [51] MMP2 30 PC, 17 CO IHC LM, Stage, Size [53] MMP2 106 PC RNA/WB DM, Stage [56] MMP2 40 PC, 10 CO IHC LM [57] MMP2 67 PC, 20 CO IHC LM, Stage, PNI [58] MMP2 33 PC, 14 CP, 13 CO ZG/WB Stage [61] MMP2 92 PC, 43 CP, 91 CO serum LM, DM [68] MMP2 32 PC IHC VI [72] MMP2 88 PC IHC OS [73] MMP3 45 PC, 10 CO IHC no [36] MMP3 18 PC, 8 CO RNA no [36] MMP3 70 PC IHC no [38] MMP3 140 PC, 12 CO IHC OS [95] MMP7 51 PC IHC/serum no [37] MMP7 29 PC IHC no [54] MMP7 88 PC IHC no [73] MMP7 45 PC, 10 CO IHC OS, LM, DIF, Stage [36] MMP7 70 PC IHC OS, Size, DIF [38] MMP7 134 PC IHC Stage, PNI, OS [122] MMP7 70 PC RNA LM, Size [97] MMP7 47 PC, 10 CO IHC OS, DM [99] MMP7 10 PC RNA OS [104] MMP7 101 PC serum OS [105] MMP7 39 PC IHC LM, OS [123] MMP8 75 PC, 10 CO IHC no [36] MMP8 91 PC, 41 CP, 30 CO RNA (PBMCs) no [39] MMP9 45 PC, 10 CO IHC no [36] MMP9 70 PC, 10 CO IHC no [38] MMP9 51 PC IHC/serum no [37] MMP9 91 PC, 41 CP, 30 CO RNA (PBMCs) no [39] MMP9 29 PC IHC no [54] MMP9 33 PC, 14 CP, 13 CO ZG/WB no [61] MMP9 9 PC, 9 CO MS/MS no [64] MMP9 35 PC RNA/IHC no [70] MMP9 32 PC IHC no [123] MMP9 27 PC IHC no [124] MMP9 62 PC, 16 CO IHC PNI, LM, Stage, Size [66] MMP9 63 PC IHC VI, OS, LM, DM [125] MMP9 62 PC IHC LM, OS [126] MMP9 32 PC, 31 CP ELISA on tissue LM [48] MMP9 30 PC, 17 CO IHC LM, Stage, Size [53] MMP9 36 PC IHC LM, DM [63] MMP9 78 PC, 45 CP, 70 CO serum OS [65] Biology 2020, 9, 80 11 of 21

Table 2. Cont.

Member Patient Number Method Correlation Reference MMP9 103 PC, 6 CO IHC OS, LM, DM, VI, Stage [67] MMP9 32 PC IHC VI [72] MMP9 88 PC IHC OS, DF, DM [73] MMP10 51 PC IHC/serum no [37] MMP11 75 PC, 10 CO IHC OS, LM, DIF, Size [36] MMP11 not indicated RNA OS [92] MMP12 75 PC, 10 CO IHC no [36] MMP12 39 PC, 13 CO RNA/WB/IHC OS, LM, Stage [111] MMP13 60 PC IHC LM [41] MMP14 70 PC IHC no [38] MMP14 75 PC, 10 CO IHC no [36] MMP14 37 PC RNA/IHC no [70] PNI, LM, DM, MMP15 78 PC IHC OS, DF, [127] Stage MMP19 102 PC IHC OS, DF, PNI, Stage [112] MMP20 102 PC IHC OS, DF, Stage, PNI [112] MMP21 25 PC, 18 CO IHC no [106] MMP26 25 PC, 18 CO IHC LM [106] MMP28 not indicated RNA OS [113] Pancreatic cancer (PC); pancreatitis (CP); healthy control (CO); benign tumor (BT); immunohistochemistry (IHC); Western blot (WB); zymography (DG); overall survival (OS); disease-free survival (DF); lymph node metastasis (LM); perineural invasion (PNI); venous invasion (VI); distant metastasis (DM); differentiation (DIF). Biology 2020, 9, 80 12 of 21

Table 3. Experimental animal models that target MMPs.

Target Model “Treatment” Result Reference MMP1 Sciatic nerve invasion shMMP1 PANC1 cells Reduced perineural invasion [33] Increased gemcitabine sensitivity, MMP2/9? Orthotopic injection HPAC cells Batimastat (day 7 till death/sacrifice) [74] − No effect single treatment Orthotopic injection HPAC cells Batimastat (day 4 till death/sacrifice) Reduced tumor growth, metastasis and death [75] − Orthotopic injection HPAC cells Batimastat (day 7 till death/sacrifice) Reduced local invasion and death [76] Orthotopic injection HPAC cells Batimastat (day 7 till death/sacrifice) Reduced tumor weight [77] Injection AsPC1 or Capan-1 cells in spleen Batimastat (day 7 till day 14) Reduced metastasis and death [78] − Subcutaneous injection SW1990 cells MMI-166 from day 7 till sacrifice at day 28 Reduced tumor growth [79] Orthotopic injection PGHAM cells (Syrian hamster) MMI-166 (day 1 till sacrifice) Reduced tumor growth, liver metastasis and MVD [80] BOP injections (Syrian hamster) RO28-2653 (week 6 till week 14) Reduced liver metastasis, No effect death [81] BOP injections (Syrian hamster) OPB-3206 in diet from day 48 till sacrifice Reduced invasive carcinoma [82] Subcutaneous injection Panc02 or MIAPaca2 cells SB-3CT (day 1 till sacrifice) Reduced lung metastasis [83] MMP2 Subcutaneous injection organoid and PSC shMMP2 PSC Reduced tumor growth [84] Subcutaneous injection PANC-1 or CFPAC-1 cells MMP2 blocking peptides after tumor take Reduced growth and MVD [85] MMP3 Kras(G12D) mice MMP3 overexpression Increased neoplastic alterations [94] MMP7 Ductal ligation MMP7 deficient mice Reduced ductal metaplasia [98] Pfta1-Cre/KrasG12D mice MMP7 deficiency No effect acinar to ductal metaplasia [105] Pdx1-Crelate/KrasG12D mice MMP7 deficiency Reduced tumor development [105] Pdx1-CreLate/KrasG12D/p53f/+ mice MMP7 deficiency Reduced tumor growth and metastasis [105] Tail vein injection PANC1 cells shMMP7 Reduced liver and lung metastasis [101] SCTPSPA (day 2 till day 25) Reduced lung metastasis [101] − MMP9 Subcutaneous injection Panc02 cells MMP9 deficient mice Reduced lung metastasis [83] Orthotopic injection Panc02 cells MMP9 overexpression Enhanced tumor growth, No effect metastasis [86] Subcutaneous injection AsPC-1 cells aMMP9 antibody AB0046 (day 1 till day 14) No effect on tumor weight [87] Increased gemcitabine/nab-paclitaxel sensitivity, Injection AsPC-1 cells in peritoneal cavity aMMP9 antibody AB0046 (day 14 till day 56) [87] No effect metastasis Subcutaneous injection Capan-1 cells Doxycycline (day 1 till day 14) Reduced growth and MVD [88] Orthotopic injection L3.6pl cells MMP9 deficient mice Reduced tumor take, growth and MVD [89] Pdx-1+/Cre;KrasG12D;Trp53 mice MMP9 deficiency Increased progression and invasive growth [90] Intravenous injection 9801 or Panc02 cells MMP9 deficient mice Increased metastasis [90] MMP14 Subcutaneous injection organoid and PSC shMMP14 PSC Reduced tumor growth [84] KrasG12D mice MMP14 overexpression Increased number of PanIN lesions [107] Reduced gemcitabine sensitivity, Subcutaneous injection PANC1 or HPAF-II cells MMP14 overexpression [108] No effect single treatment Orthotopic injection DanG or BxPc3 cells MTCBP-1 overexpression Reduced metastasis, No effect tumor growth [109] Note: All experiments were performed using mice unless indicated otherwise. Biology 2020, 9, 80 13 of 21

4. Conclusions The potential clinical relevance of MMPs in PDAC has largely been addressed using patient-derived tumor material. These studies show a rather consistent picture with respect to MMP overexpression in tumors compared to control sections, although almost 25% of the studies do not show significant differences between patients and controls. However, the association of MMP overexpression with clinical characteristics is not as convincing as suggested in the literature. Half of the studies show that high MMP levels are associated with (lymph node) metastasis and reduced survival, whereas the other half of the studies do not show any correlation with clinical characteristics. Patient-derived data do not, therefore, seem to allow firm conclusions that MMP expression levels (in general) are associated with PDAC progression and poor prognosis to be drawn, especially when considering that publication bias may have resulted in negative studies not being published. Initial preclinical experimental animal models using broad spectrum MMP inhibitors are more in line with the general role of MMPs in PDAC progression, as different inhibitors limit tumor growth and metastasis in subcutaneous, orthotopic and spontaneous PDAC models. The contribution of individual MMPs in PDAC progression is, however, not very well established. Only MMP2, MMP7 and MMP14 are shown to potentiate tumor growth and/or metastasis in multiple independent papers. For others, the literature is conflicting or missing and no clear conclusions can be drawn. Importantly, however, conflicting results do not indicate that the individual MMPs have no effect in PDAC. The biology of PDAC and MMP is complex and MMPs may act in a context-dependent manner, with both tumor-promoting and tumor-inhibiting effects. The conflicting role of MMP9 serves as an excellent example for this notion. The data rather convincingly show that tumor MMP9 expression drives PDAC progression, but systemic MMP9 ablation triggers invasive growth and metastasis by blocking MMP9-dependent tumor-inhibiting effects in the marrow. Despite the presence of a large range of MMP-deficient animals and the relative ease of generating MMP deficient cells with CRISPR technology, the majority of MMPs have not been studied in preclinical PDAC animal models. To fully appreciate the importance of individual MMPs in PDAC progression and to assess their potential clinical relevance, we have to await studies that combine (pharmacological inhibition in) genetic Kras-driven spontaneous models with subcutaneous and/or orthotopic models, in which MMPs are specifically depleted in stromal or tumor cells. In particular, experiments that address pharmacological treatment with specific MMP inhibitors after tumors could turn out to be invaluable for establishing the context-dependent role of individual MMPs in PDAC. Before such studies have been performed, we should be careful not to generalize the available literature. Although broad spectrum MMP inhibitors limit PDAC progression in preclinical animal models [73–82], they seem to lack efficacy in a clinical setting [115,116]. This disparity between preclinical data and clinical trials can be attributed to several factors—for instance, differences in pharmacokinetics, pharmacodynamics and metabolism and the failure to accurately model the tumor microenvironment [128]. In particular, xenograft models, which lack a functional immune system, show a reduced complexity and cellular diversity compared to human disease models. Moreover, the degree of aneuploidy in human tumors results in great variety within inter-tumoral gene modifications, in a different manner compared to how it occurs in mice [129,130]. All of these species-related differences limit the capacity of preclinical mouse models to accurately predict the response of MMP inhibitors in PDAC patients. In conclusion, based on our systematic review on the role of matrix metalloproteases in PDAC, we conclude that the available literature is not as consistent as envisioned and that, although individual matrix metalloproteases seem to contribute to PDAC growth and metastasis, our review does not support the generalized notion that matrix metalloproteases drive PDAC progression.

Supplementary Materials: The following are available online at http://www.mdpi.com/2079-7737/9/4/80/s1, Table S1: Search terms used and number of papers retrieved. Biology 2020, 9, 80 14 of 21

Funding: This research was funded by grants from the Dutch Cancer Foundation (UVA 2017-11174 and UVA 2014-6782) and the Netherlands Organization for Scientific Research (VENI grant 016.186.046). Conflicts of Interest: The authors declare no conflict of interest. M.F.B. has acted as a consultant to Servier, and received research funding from Celgene.

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